DeepMEND: Reliable and Scalable Network Metadata Geolocation from Base Station Positions

Abstract

Metadata geolocation, i.e., mapping information collected at a cellular Base Station (BS) to the geographical area it covers, is a central operation in the production of statistics from mobile network measurements. This task requires modeling the probability that a device attached to a BS is at a specific location, and is presently addressed with simplistic approximations based on Voronoi tessellations. As we show, Voronoi cells exhibit poor accuracy compared to real-world geolocation data, which can, in turn, reduce the reliability of research results. We propose a new approach for data-driven metadata geolocation based on a teacher-student paradigm that combines probabilistic inference and deep learning. Our Deepmend model: ($i$) only needs BS positions as input, exactly like Voronoi tessellations; (ii) produces geolocation maps that are 56% and 33% more accurate than legacy Voronoi and their state-of-the-art VoronoiBoost calibration, respectively; and, (iii) generates geolocation data for thousands of BSs in minutes. We assess its accuracy against real-world multi-city geolocation data of 5, 947 BSs provided by a network operator, and demonstrate the impact of its enhanced metadata geolocation on two applications use cases.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 3 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.